Member of Technical Staff, Performance Optimization

The role

You will optimize performance at every layer of the stack from low-level GPU kernels to large-scale distributed systems. This role focuses on maximizing performance for LLM, VLM, and video model workloads.

What you will do

• Optimize system and GPU performance for high-throughput AI workloads
• Analyze and improve latency, throughput, memory usage, and compute efficiency
• Profile system performance to detect and resolve GPU- and kernel-level bottlenecks
• Implement low-level optimizations using CUDA, Triton, and other performance tooling
• Drive improvements in execution speed and resource utilization for large-scale model workloads
• Collaborate with ML researchers to co-design and tune model architectures for hardware efficiency
• Improve support for mixed precision, quantization, and model graph optimization
• Build and maintain performance benchmarking and monitoring infrastructure
• Scale inference and training systems across multi-GPU, multi-node environments
• Evaluate and integrate optimizations for emerging hardware accelerators and specialized runtimes

Required qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience
• 5+ years of experience working on performance optimization or high-performance computing systems
• Proficiency in CUDA or ROCm and experience with GPU profiling tools (e.g., Nsight, nvprof, CUPTI)
• Familiarity with PyTorch and performance-critical model execution
• Experience with distributed system debugging and optimization in multi-GPU environments
• Deep understanding of GPU architecture, parallel programming models, and compute kernels

Nice to have

• Master’s or PhD in Computer Science, Electrical Engineering, or a related field
• Experience optimizing large models for training and inference (LLMs, VLMs, or video models)
• Knowledge of compiler stacks or ML compilers (e.g., torch.compile, Triton, XLA)
• Contributions to open-source ML or HPC infrastructure
• Familiarity with cloud-scale AI infrastructure and orchestration tools (e.g., Kubernetes)
• Background in ML systems engineering or hardware-aware model design

About the company

Fireworks is a platform for specialized intelligence that enables companies to build, train, and serve AI models. It is a Series D company valued at $17.5 billion.